Romania
ERASMUS MUNDUS
Quy Nhon University
Viet Nam
NGUYỄN THÀNH ĐẠT
SIX SIGMA-BASED KNOWLEDGE MANAGEMENT AND
ITS APPLICATION IN IT. SYSTEMS MANAGEMENT
Thesis evaluation commission / Comisia de evaluare a tezei de doctorat:
President / Președinte:
Prof. univ. dr. ing. Liviu-Ion ROȘCA, Universitatea “Lucian Blaga” din Sibiu
Members / Membrii:
Prof. univ. dr. ing. Claudiu Vasile KIFOR Conducător științific,
Universitatea “Lucian Blaga” din Sibiu.
Prof. univ. dr. ing. Anca DRĂGHICI Universitatea Politehnica Timișoara.
Prof. univ. dr. ing. Sorin Gabriel POPESCU Universitatea Tehnică din Cluj-Napoca.
Prof. univ. dr. ing. Marius CIOCA Universitatea “Lucian Blaga” din Sibiu.
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TABLE OF CONTENTS
ABSTRACT ................................................................................................................ I
ACKNOWLEDGEMENT ......................................................................................... III
TABLE OF CONTENTS ........................................................................................... IV
LIST OF FIGURES .................................................................................................. VII
LIST OF TABLES ..................................................................................................... XI
LIST OF ABBREVIATIONS ................................................................................. XIII
CHAPTER 1. INTRODUCTION ................................................................................ 1
1.1 THE PROBLEM STATEMENT .......................................................................................... 2
1.2 RESEARCH OBJECTIVES ................................................................................................ 3
1.3 WHY SIX SIGMA AND DMAIC ..................................................................................... 4
1.4 WHY KNOWLEDGE MANAGEMENT? ............................................................................. 4
1.5 INNOVATION ELEMENTS ............................................................................................... 5
1.6 RESEARCH METHODOLOGY .......................................................................................... 6
1.7 ASSUMPTIONS ............................................................................................................... 7
1.8 LIMITATIONS OF RESEARCH .......................................................................................... 7
1.9 DELIMITATIONS ............................................................................................................ 8
1.10 THE THESIS’S STRUCTURE ............................................................................................ 8
CHAPTER 2. RELATED WORKS .......................................................................... 10
2.1 SIX SIGMA, DMAIC, AND FMEA .............................................................................. 10
2.1.1 What is Six Sigma? ........................................................................................ 10
2.1.2 Six Sigma implementation and tools ............................................................. 15
2.1.3 DMAIC .......................................................................................................... 21
2.1.4 The role of project members .......................................................................... 25
2.1.5 Failure Mode and Effect Analysis ................................................................. 26
2.2 KNOWLEDGE MANAGEMENT AND ONTOLOGY ........................................................... 37
2.2.1 Knowledge and Knowledge classification ..................................................... 37
2.2.2 Knowledge representation .............................................................................. 44
2.2.3 Knowledge Portal ........................................................................................... 45
2.2.4 Knowledge Management ............................................................................... 47
2.2.5 Processes of knowledge management ............................................................ 50
2.2.6 Conceptual Models of Knowledge management (Dalkir, 2005) ................... 51
2.2.7 Ontology and Ontology Development ........................................................... 56
2.3 INTEGRATING KNOWLEDGE MANAGEMENT WITH SIX SIGMA .................................... 77
2.3.1 Integrated Models of Knowledge management and Six Sigma ..................... 77
2.3.2 Ontology-based approaches for KM in Six Sigma methodology .................. 81
2.4 IT. SYSTEMS MANAGEMENT AND SERVER FAILURE .................................................... 82
2.4.1 IT. Systems management ............................................................................... 82
2.4.2 Client/Server System...................................................................................... 84
2.4.3 Enhancing Server Availability ....................................................................... 91
2.4.4 Server Event Log ........................................................................................... 92
2.4.5 Log Parsers ................................................................................................... 102
CHAPTER 3. SIX SIGAM-BASED KNOWLEDGE MANAGEMENT ............. 105
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3.1 PROPOSED MODEL ARCHITECTURE ............................................................................ 105
3.2 ACCUMULATING AND REUSING DMAIC KNOWLEDGE .............................................. 107
3.2.1 K-Creation/Acquisition ................................................................................ 107
3.2.2 K-Structure & Storage ................................................................................. 108
3.2.3 K-Protection ................................................................................................. 108
3.2.4 K-Application............................................................................................... 109
3.3 THE PROPOSED TOOLS TO SUPPORT ACTIVITIES OF KNOWLEDGE MANAGEMENT ....... 109
3.4 CHAPTER CONCLUSION ............................................................................................ 110
CHAPTER 4. KPD – A SIX SIGMA KNOWLEDGE PORTAL ......................... 111
4.1 WHAT IS KNOWLEDGE PORTAL ................................................................................ 112
4.2 REQUIREMENTS AND FUNCTIONALITIES FOR KNOWLEDGE PORTAL ......................... 113
4.3 RELATED KNOWLEDGE PORTALS ............................................................................. 114
4.4 A PROPOSED KNOWLEDGE PORTAL FOR DMAIC PROCESSES .................................. 116
4.4.1 The knowledge portal architecture ............................................................... 116
4.4.2 A procedure for KPD deployment ............................................................... 118
4.4.3 Data collection ............................................................................................. 119
4.4.4 Ontology-based knowledge representation .................................................. 120
4.4.5 Deployment of Knowledge Reasoner module ............................................. 122
4.4.6 Deployment of CMS and LMS .................................................................... 123
4.4.7 Supporting document ................................................................................... 124
4.5 SUCCESSFUL ASPECT OF APPLYING KPD ................................................................. 124
4.6 CONCLUSION ............................................................................................................ 127
CHAPTER 5. SIX SIGMA-BASED IT SYSTEMS MANAGEMENT ................ 128
5.1 A PROPOSED SYSTEM MODEL .................................................................................. 129
5.2 SERVER EVENT LOGS ............................................................................................... 130
5.3 KNOWLEDGE PORTAL FOR SERVER FAILURE MANAGEMENT ................................... 134
5.4 LOG PARSER FOR SELO ........................................................................................... 135
5.4.1 Log Parser 2.2. - A Decrypted Engine ......................................................... 136
5.4.2 A querying module (PHP API for Log Parser 2.2) ...................................... 138
5.5 SELO – FMEA-BASED ONTOLOGY FOR SEL ........................................................... 139
5.5.1 The methods to build SELO ......................................................................... 141
5.5.2 SELO Development Procedure .................................................................... 144
5.5.3 The Tool and Language to Build SELO ...................................................... 159
5.6 SELO PARSER - AUTOMATIC ONTOLOGY POPULATION FROM AN EVENT LOG ......... 160
5.7 SELO REASONER – KNOWLEDGE INFERENCE AND REPORTS GENERATION.............. 165
5.7.1 SPARQL Query ........................................................................................... 166
5.7.2 SPARQL Queries to Support Reports .......................................................... 167
5.8 CONCLUSION ............................................................................................................ 167
CHAPTER 6. EXPERIMENTS, RESULTS AND EVALUATION ..................... 169
6.1 A DISCUSSION ON SUSTAINABILITY OF OKMD MODEL ............................................ 169
6.2 KNOWLEDGE PORTAL FOR DMAIC .......................................................................... 171
6.3 PERFORMANCE AND ACCURACY OF SELO TOOLS ................................................... 178
6.3.1 EXPERIMENTAL PARAMETERS .................................................................................. 178
6.3.2 SEL Ontology .............................................................................................. 180
6.3.3 SELO Parser ................................................................................................. 183
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6.3.4 SELO Reasoner ............................................................................................ 186
6.3.5 An example of SELO Model ....................................................................... 188
6.3.6 Results and Evaluation ................................................................................. 190
6.4 SELO KNOWLEDGE BASE ........................................................................................ 200
6.4.1 Validate SELO based OntoQA technique .................................................... 200
6.4.2 Based on the Similar Approaches ................................................................ 203
6.5 AN EVALUATION OF OKMD MODEL BASED ON EXPERTS’ OPINION ........................ 205
6.5.1 The Survey’s Parameters ............................................................................. 205
6.5.2 The Results and Discussion ......................................................................... 206
6.6 CHAPTER CONCLUSION ............................................................................................ 216
CHAPTER 7. CONCLUSION, CONTRUBITION, AND FUTURE WORKS... 217
7.1 RESEARCH OVERVIEW .............................................................................................. 218
7.2 RESEARCH FINDINGS ................................................................................................ 221
7.3 RESEARCH CONTRIBUTIONS ..................................................................................... 222
7.3.1 Theoretical Contribution .............................................................................. 223
7.3.2 Practical Contributions ................................................................................. 223
7.3.3 Scientific Contributions ............................................................................... 224
7.4 FUTURE RESEARCH DIRECTIONS .............................................................................. 224
REFERENCES .......................................................................................................... 226
ANNEXES ............................................................................................................ 241
ANNEX 1. QUESTIONNAIRE .............................................................................................. 241
ANNEX 2. LIST OF PUBLICATIONS .................................................................................... 251
ANNEX 3. SPARQL QUERIES .......................................................................................... 253
ANNEX 4. CURRICULUM VITAE ........................................................................................ 254
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ABSTRACT
Nowadays, information has involved in all activities and fields. Several organizations
have been constructing information technology systems to collect, organize, store, and
communicate information in order to strengthen the competitiveness, improve the quality of
products and services, and aim at a sustainable development. An IT system includes several
computers, servers, and other hardware (network devices, printers, projectors…) that are
connected together. Hence, it is imperative to keep your enterprise's server system up and
running, and solutions for eliminating errors from IT systems are necessary.
Six Sigma is one of effective methodologies that can support to such solutions.
DMAIC and FMEA are the problem-solving tools in Six Sigma system. Effectiveness of
DMAIC and FMEA depend up solutions, innovations, or plans proposed by experts or
members in a Six Sigma project. However, knowledge created by Six Sigma tools is difficult
to access or reuse..
This research aims at building a new model to manage knowledge created by Six
Sigma tools and to investigate applicability of the model in management of IT. Systems. We
are going to propose an integrated model of Six Sigma DMAIC and Knowledge management
to resolve the research problem. The proposed tools related to the model are going to be
experimented and evaluated carefully, scientifically and throughout. The results of this
research is going to reveal the costs and time effectiveness and applicability of the proposed
solution. Evaluation is going to be conducted completely based on literature, comparable
analysis, experiments, and experts’ opinion. Finally, the conclusion of the thesis is going to
reveal scientific contributions and innovation of this research to the field of Quality
Improvement and Information Technology.
Chapter 1. INTRODUCTION
1.1 The Problem Statement
Many solutions of integrating knowledge management and Six Sigma have been
applying into many fields such as healthcare, automative, industry, textile… However, such
solution to apply to IT systems management is not still found in literature or developed yet.
Hence, a solution of Six Sigma-based knowledge management and its supporting tools that
can apply into IT systems management are also a research problem of concern.
1.2 Research Objectives
This research includes several objectives that aim at proposing a solution of Six Sigma-
based knowledge management and applying the model into IT systems management. The
main objectives of this research include (1) designing a proposed model of knowledge
management for Six Sigma DMAIC processes, (2) building a Knowledge Portal, (3) building
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a knowledge base of server breakdown/failure, (4) developing tools that support to the
knowledge base.
1.3 Research Methodology
Qualitative and Quantitative methodology are important and common techniques
applied in this research. Both of them use the large amount of the empirical data collected
from research activities to compare features of the evidence they have gathered internally or
with related evidence. Non-experimental and experimental research are also two main
methods in research activities. Basing on the experimental research method, the researcher
proposed an experimental design for collecting data to test the hypotheses.
1.4 Limitations of research
This research does not cover all of related models. The proposed tools are developed
for a particular process only. Ontologies and tools are developed based on some available and
free tools and languages. The reality impact of the proposed model for Six Sigma projects is
limited, is should be validated in reality
1.5 The Thesis’s Structure
Chapter 1: Introduction, Chapter 2: Related Works, Chapter 3: Six Sigma-based
Knowledge Management, Chapter 4: KPD – A Six Sigma Knowledge Portal, Chapter 5: Six
Sigma-based Server Failure Management, Chapter 6: Experiments Results and Evaluation,
and Chapter 7: Conclusion, Contribution, and Figure Works.
Chapter 2. RELATED WORKS
2.1 Six Sigma, DMAIC, and FMEA
Six Sigma is a quality improvement methodology developed by Motorola in 1980s.
Six Sigma uses a five-step breakthrough strategy proposed by (Sung H. Park, 2003) to define,
measure, analyze, improve and control (DMAIC) defects of existing products, processes, or
services which are defined as anything that causes dissatisfaction of customer (Revere &
Black, 2003). It also ultilizes Failure Mode and Effects Analysis method to evaluate possible
errors of processes or products and their effects and determine recommended actions that
reduce the possible errors.
2.2 Knowledge Management and Ontology
Oxford Dictionaries defines knowledge as “facts, information, and skills acquired
through experience or education; the theoretical or practical understanding of a subject” or
“awareness or familiarity gained by experience of a fact or situation”.
Knowledge can be represented by one of the popular approaces in which Ontology-
based approach allow representing both tacit and explicit knowledge in hierarchical structure.
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Ontology represent knowledge based on concepts, relationships of the concepts, properties,
rules, restricts, and constraints.Knowledge can be transferred, stored, and retrieved via a
Knowledge Portal.
Knowledge Portal also supports a process of knowledge management which is “the
process of applying a systematic approach to the capture, structure, management, and
dissemination of knowledge through an organization in order to work faster, reuse best
practices, and reduce costly rework from project to project” (Dalkir, 2005), (Nonaka &
Takeuchi, 1995).
2.3 Integrating Knowledge Management With Six Sigma
Several integrated models of KM and Six Sigma have been proposed such as a process
model of knowledge creation opportunities, IKR model, DMAIC-KM model, and
SECI/SIPOC Continuous Loop model. One trait that is common both KM and Six Sigma is
to create valuable knowledge in the process of management. Recently, using Ontology to
manage knowledge created by problem solving tools of Six Sigma is considered as an
emerging approach.
2.4 IT. Systems management and Server Failure
In organizations, IT systems are known as computer systems constructed to organize,
store, and provide information and information service to organizational activities such as
Email system, Web system, Application system, Database system or Data Center… IT
systems management is “the activity of identifying and integrating various products and
processes in order to provide a stable and responsive IT environment” (Schiesser, 2010). The
main objective of IT systems management is to bring stability and responsiveness to IT
systems in 24 hours per day, 7 days per week. IT systems management aims at enhancing
availability of whole system and ensures that IT systems are always ready to overcome a big
amount of challenges and problems coming from several components of the systems.
Server failure impacts negatively on server availability and therefore results in outage
or breakdown of server applications and services, degrading user experience and eventually
causing lost revenue for businesses (Manish, Mishra, & Fetzer, 2008).
Chapter 3. SIX SIGAM-BASED KNOWLEDGE MANAGEMENT
3.1 Proposed model architecture
The proposed model (Ontology-based Knowledge Management process for DMAIC -
OKMD) (Figure 3-1) is an integrated conceptual model that combines activities of DMAIC
process, knowledge management and ontology engineering. The ultimate goal of OKMD
model is to facilitate the knowledge management process for DMAIC deployment.
Knowledge created during DMAIC execution is accumulated into a knowledge base by
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Ontology techniques, and then is distributed to knowledge workers through a Knowledge
Portal. Thereby, available knowledge resource from DMAIC improvement process will be
preserved and reused sustainably. The activities of a knowledge management procedure
(Figure 3-2) comprising Knowledge Creation/Acquisition, Knowledge Structure & Storage,
Knowledge Protection, and Knowledge Application (Gold, Albert, & Arvind, 2001) are
executed continuously within each of five DMAIC steps consisting of Define, Measure,
Analysis, Improve, and Control.
3.2 Activities of OKMD model
3.2.1 K-Creation/Acquisition
The activities of K-Creation/Acquisition stage (arrow path 1 in Figure 3-3) is to obtain
new knowledge (Gold, Albert, & Arvind, 2001). The stage should be started at the Gate
review section of every DMAIC step where members of project team such as Black Belt,
Green Belt, domain experts discuss and review problem-solving solutions or improvement
plans basing on reports and documents created. The support of Knowledge Portal allows them
to submit or upload their reports, documents, writings, and relevant files to Knowledge Portal.
Figure 3-3. KM activities in OKMD model
3.2.2 K-Structure & Storage
K-Structure & Storage (arrow path 2) aims at cumulating new knowledge into sub-
knowledge bases based on Ontology Engineering.
3.2.3 K-Protection
K-Protection (arrow path 3) is to prevent illegal or inappropriate behaviors of web
users who are querying knowledge available on Knowledge Portal.
3.2.4 K-Application
K-Application (arrow path 4) is necessary to share and reuse created knowledge.
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3.3 The proposed tools to support activities of knowledge management
In order for OKMD model to be implemeted and applicable effectively, many tools
that support to its activities are proposed that presented in Table 3-1. Generally, the support
tools are adapted from Six Sigma guideline, (ISO13053-1, 2011), interview, word processing
softwares, Ontology building tools, programing languagues, and functionalities of
Knowledge Portal comprising forums, chat rooms, modules for uploading and downloading
files, search engine, email, user account, database, and inference/reason engine.
Chapter 4. KPD – A SIX SIGMA KNOWLEDGE PORTAL
4.1 A Proposed Knowledge Portal for DMAIC processes
4.1.1 The knowledge portal architecture
Interface layer that provides web-based interfaces to its users, presents content of
KPD, and supports user login/authorization.
Service layer that provides essential functionalities for content and knowledge
management described in Figure 4-2. Basing literature review, functionalities of KPD are
grouped into five groups:
Content Management: A group of functionalities for managing and broadcasting
organizational information, resources, and links to its customers and employees.
Knowledge Exchange: Functionalities for activities of knowledge exchange involving
communication and learning, i.e. chat or discussion, organizing online courses and
presentation. It also is a place for collecting reports created by Six Sigma tools.
Knowledge Dissemination. A group of functionalities that enables to share and retrieve
DMAIC knowledge available and DMAIC reports for the reuse or evaluation purposes.
Supporting Document: Functionalities to search guidance, documents, and materials
of IT.
Administration. The functionalities for administrators and IT specialists. They are
divided into three sub-groups: User, System, and Configuration Management to create and
control the security policies of various types of user, to support activities of system
management for servers, databases, and SPARQL endpoint, and to create as well as customize
flexibly modules and interface of Web sites by itself.
Data layer is built as a database in order to store organizational knowledge. In this
layer all documents, multimedia files, data of courses, and reports are stored. It is also
connected to knowledge bases in which knowledge is created from DMAIC reports and
represented by Ontologies and provides query services based on MySQL and SPARQL.
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4.1.2 Ontology-based knowledge representation
Simply, a DMAIC report is structured into columns, rows, and values of a table. The
table is then translated into a sub-network or a branch of Ontology graph. Each row name of
the table is translated into an instance name. Each column name can be translated into either
a class name or a name of Data-type property. A value (a cell in the table) is mapped to a
value of a Data-type property. Each property describes a relation from a class to a class or
from a class to a value.
4.1.3 Knowledge Reasoner module
Figure 4-5. (a) Architecture of K-Reasoner module.
In order to search and infer DMAIC knowledge, a Knowledge Reasoner (K-Reasoner)
module is developed. It enables to get and analyze query requirements, to connect to
Ontologies through SPARQL endpoint, to generate and perform SPARQL queries, and to
present query results found based various types of search such as Quick Search, Basic Search,
Advanced Search, and Question-based Search (Figure 4-5.a).
Chapter 5. SIX SIGMA-BASED IT SYSTEMS MANAGEMENT
5.1 A Proposed System Model
Figure 5-1. SELO Model
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The proposed solution (i.e. SELO) is a combination of FMEA methodology,
techniques of log mining and Ontology building. It may be illustrated based on a system
model like Figure 5-1, and designed to enrich a knowledge base in which knowledge of server
events and their solutions is acquired. SELO model introduces a process to transfers
knowledge from event logs to a knowledge base. Specially, event logs collected from a server
are first decoded to convert to the text-based format a data table by a Log Parser. Output data
collected from the Log Parser is then used to populate instances of SEL ontology using SELO
Parser. SEL ontology and its instances form a knowledge base that enables to share and reuse
among individuals and computers. SELO Reasoner should be used to extract the knowledge
from the knowledge base to FMEA reports and to support experts as well as administrators to
insert or update the solutions of the failure events. The solutions can be either identified based
on deploying FMEA methodology or the available solutions that have overcome the failure
events. Furthermore, SELO Reasoner is responsible for updating the knowledge base with
taken solutions or actions. Finally, a user who accesses the knowledge base can send requests
to SELO Reasoner to look for solutions for some event. In this case, SELO Reasoner should
return a FMEA-based report that includes information of relevant events and solutions, and
the schema of SEL ontology that facilitates learning of SELO knowledge. Besides, it also
allows a user to create and send SPARQL queries, and to display reports formatted based on
structure of other DMAIC tools such as FMEA or Pareto chart.
5.2 SELO – FMEA-based Ontology for SEL
5.2.1 SELO Development Procedure
Figure 5-19. UML-based schema for SELO representation
Sever Event Log Ontology (SELO) is a schema to represent semantically and
systematically concepts and relationships of the concepts involved in server events and
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solutions for the server events. It is designed based on the structure of event logs (EVT format)
and FEMA report. The fields of the event log and the header of FMEA report are mapped to
the main components of SELO consisting of classes and properties. The relationships between
the EVT fields as well as between main components of MFEA are used to define the
relationships and restrictions of SELO’s classes and properties (Figure 5-18, Figure 5-19).
5.3 SELO Parser - Automatic Ontology Population from an Event Log
On the basis of the proposed requirements, SELO Parser is developed to generate
automatically instances of SELO from a server event log. We propose an algorithm for the
Parser. In the proposed algorithm, it assumes that SELO includes a list of class names Ck, an
event log comprises a list of event ei, and a list of fields with the column/field names cj. Since
Ck is the name of a kth class, and ck.newinstance is used when a new instance is created for the
class ck. The algorithm is described as the above
5.4 SELO Reasoner – Knowledge Inference and Reports Generation
In order to query and infer SELO knowledge, we proposed an inference engine called
SELO Reasoner whose architecture is similar to our previous work for KPD. It is written in
PHP language and SPARQL. Its algorithm consists of functions that enable to get and analyse
query requirements, to connect to Ontologies through SPARQL endpoint, to generate and
perform SPARQL queries, and to present query results found (Figure 5-21).
𝒇𝒐𝒓𝒆𝒂𝒄𝒉 𝑒𝑣𝑒𝑛𝑡 𝑒𝑖 ∈ 𝑒𝑣𝑒𝑛𝑡 log 𝐿 𝑑𝑜
𝒇𝒐𝒓𝒆𝒂𝒄𝒉 𝑐𝑜𝑙𝑢𝑚𝑛 𝑐𝑗 ∈ 𝑒𝑣𝑒𝑛𝑡 𝑒𝑖 do
𝒊𝒇 ∃𝑐𝑗 𝑐𝑜𝑟𝑒𝑠𝑝𝑜𝑛𝑑𝑖𝑛𝑔 𝑡𝑜 𝐶𝑘 ∈ 𝑐𝑙𝑎𝑠𝑠_𝑙𝑖𝑠𝑡 𝐶 𝒕𝒉𝒆𝒏
𝒊𝒇 𝑛𝑢𝑚𝑏𝑒𝑟_𝑚𝑒𝑚𝑏𝑒𝑟_𝑜𝑓(𝐶𝑘) = 0 𝒕𝒉𝒆𝒏
𝑐𝑟𝑒𝑎𝑡𝑒 𝑎 𝑛𝑒𝑤 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑖𝑘 𝑜𝑓 𝑐𝑙𝑎𝑠𝑠 𝐶𝑘 𝑎𝑛𝑑 𝑢𝑝𝑑𝑎𝑡𝑒 𝑖𝑡𝑠 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠;
𝐶𝑘 . 𝑛𝑒𝑤𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑡𝑟𝑢𝑒;
𝒆𝒍𝒔𝒆 𝒊𝒇 𝑒𝑖 𝑐𝑗 ≠ 𝑒(𝑖−1) 𝑐𝑗 𝑜𝑟 (𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒_𝑑𝑜𝑚𝑎𝑖𝑛(𝐶𝑘). 𝑛𝑒𝑤𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑡𝑟𝑢𝑒) 𝒕𝒉𝒆𝒏
𝑠𝑎𝑣𝑒 𝑡ℎ𝑒 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑐𝑙𝑎𝑠𝑠 𝐶𝑘;
𝑐𝑟𝑒𝑎𝑡𝑒 𝑎 𝑛𝑒𝑤 𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 𝑖𝑘 𝑜𝑓 𝑐𝑙𝑎𝑠𝑠 𝐶𝑘 𝑎𝑛𝑑 𝑢𝑝𝑑𝑎𝑡𝑒 𝑖𝑡𝑠 𝑎𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑠;
𝐶𝑘 . 𝑛𝑒𝑤𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑡𝑟𝑢𝑒;
𝒆𝒍𝒔𝒆 𝐶𝑘. 𝑛𝑒𝑤𝑖𝑛𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑓𝑎𝑙𝑠𝑒;
𝒆𝒏𝒅𝒊𝒇
𝒆𝒏𝒅𝒊𝒇
General Algorithm: Populating all instances from an EVT Log
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Chapter 6. EXPERIMENTS, RESULTS AND EVALUATION
6.1 Sustainability of OKMD model
Sustainable aspects of DMAIC process in OKMD model are discussed through criteria
presented by (Harris, 2000), (Brundtland, 1987) and seven sustainable measures presented by
the authors in (Mahesh, Henrietta, Laszlo, & Jozsef, 2008) (Ansari, Holland, & Fathi, 2010).
The essential goals of sustainability are economic growth, environmental conservation, and
social equity (Aparna & Keren, 2007). Moreover, sustainability of Six Sigma DMAIC process
can be improved based on the KM process in OKMD model and seven sustainable measures
(Nguyen & Kifor, 2015).
6.2 Knowledge Portal for DMAIC
Figure 6-2. Homepage of KPD
In order to validate the proposed model, a KPD has built based on the proposed steps
of implementation (Figure 6-2). It provides funtions to collect data and knowledge from Six
Sigma tools, and to retrieve knowledge from a knowledge base based on K-Reasoner tool
(Figure 6-3).
6.3 Performance and Accuracy of SELO Tools
6.3.1 Experimental Parameters
To illustrate the proposed solution, we have built the all proposed
components/modules of SELO model including SEL Ontology, SELO Parser, and SELO
Reasoner based on the description presented in Chapter 3 of this thesis.
Table 6-1. The collected server event logs
No. EVT log EVT’s Size No. of Events
(log messages) Severities Types of Event
1 Application.evtx 20.55 MB 6,627 3 7
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2 Security.evtx 9.284 MB 13,097 2 17
3 System.evtx 20.55 MB 42,961 3 52
4 Web Server.evtx 1.092 MB 376 2 4
In our experiments, data is collected from a Web server that was running Windows
Server 2008 at a university. Data includes various types of event log comprising Web
Server.evtx, Security.evtx, Application.evtx, and System.evtx (Table 6-1). Each contains a
number of log messages (or logged events), and is used to generate instances of SELO. In the
event logs, the numbers of log messages, severity levels, and types of event are different.
They are used to experiment performance and accuracy of SELO components.
The experiments are fulfilled on a machine with 2.8-GHz Intel Core i7-4558U CPU,
8GB RAM Memory, SSD Dual 2x128GB HDD, and Microsoft Windows 10 Professional OS
64bit. All parameters are averaged after five times of experimental run. We also have
constructed SPARQL endpoints based on PHP, MySQL, and ARC2 package as well as Jena
Fuseki to evaluate performance of the designed components.
6.3.2 Results and Evaluation
a. Performance
Table 6-3. SELO’s sizes and the parsing execution time
No. EVT log EVT’s Size No. of
Events
SELO’s
size
No. of
generated
instances
The average time of parsing
Log Parser
2.2 SELO Parser
1 Application 20.55 MB 6,627 8.390 MB 23,470 <<1 0.44
2 Security 9.284 MB 13,097 8.994 MB 26,227 <<1 0.48
3 System 20.55 MB 42,961 34.14 MB 97,836 <<1 1.78
4 Web Server 1.092 MB 376 383 KB 1,059 <<1 0.04
Figure 6-26. The parsing execution time of each event log
The Table 6-3 show the parsing execution time of every event log and the size of every
SELO generated by SELO Parser. The average time of running parser varies from a low of
0.04 seconds for Web Server log to a high of 1.78 seconds for System log. SELO Parser can
parse over 97,800 log messages within 1.78 seconds. The log files with the bigger number of
log messages of event tend to be parsed for longer than the log files with the smaller number
of log messages of event.
The experiments are conducted on both SPARQL endpoints, MySQL+ACR2 and Jena
Fuseki. The experimental results are described as Figure 6-28. The bar chart illustrates the
average time of query execution on six groups of events namely, all events, information event,
Six Sigma-based KM and Its Application in IT. Systems Management
15 | P a g e
warning event, error event, success audit event, and failure audit event in four event logs.
Overall, the query execution time consumed by Jena Fuseki exceed upwards the time
consumed by MySQL+ARC2 on all event logs excepted to Web Server log.
Figure 6-28. The query execution time run on ARC2 and Jena Fuseki by types of
event
However, a contrary figure is found when we measure the FMEA report creation time
on both the SPARQL endpoints (Figure 6-29). In general, the time to create a FMEA report
on Jena is several times faster than the time on MySQL+ARC2.
0
1
2
3
4
5
6
7
All events Information Warning Error
Ru
nn
ing
tim
e in
se
co
nd
s
Type of event
Application log6.627 messages
ARC2
JENA
0
2
4
6
8
10
All events Success AuditEvent
Failure AuditEvent
Ru
nn
ing
tim
e in
se
co
nd
s
Type of event
Security log13,097 messages
ARC2
JENA
0
5
10
15
20
25
30
All events Information Warning Error
Run
nin
g tim
e in
se
co
nd
s
Type of event
System log42,961 messages
ARC2
JENA
0
0.02
0.04
0.06
0.08
0.1
0.12
All events Information Warning Error
Run
nin
g tim
e in
se
co
nd
s
Type of event
Web server log376 messages
ARC2
JENA
020406080
100120
Ru
nn
ing
tim
e in
sec
on
ds
Type of event
Application log
ARC2
JENA
020406080
100120140
All events Success AuditEvent
Failure AuditEvent
Ru
nn
ing
tim
e in
sec
on
ds
Type of event
Security logARC2
JENA
Lucian Blaga University of Sibiu
16 | P a g e
Figure 6-29. The FMEA creation time run on ARC2 and Jena Fuseki
In the next situation, we compare the running time to infer knowledge of SELO to the
running time to infer knowledge of a FMEA-based ontology (FO) proposed by the authors in
(Rehman & Claudiu, 2016) in order to evaluate the performance of our solution against their
solution.
Figure 6-30. The query running time on the number of different events
In general, SELO needs more time to query than FO in most of event logs excepted to
Web Server log. However, the numbers of items (events) in our data sets are much bigger
than that (processes) in FO’s data set. Hence, the average time to retrieve items for our
solution much better than FO approach (Figure 6-30).
In the last situation, we evaluate the parsing execution time of SELO Reasoner on all
event logs by varying the number of log messages, and compare the experimental results to
other similar Parsers. In (Pinjia, Jieming, Shilin, Jian, & Michael, 2016). We choosed 2 of 4
log parsing methods (LogSig and SLCT) and 3 of 5 event datasets (BGL, Zookeeper, and
Proxifier) to compare to our solution. Figure 6-32 shows the average parsing execution time
0100200300400500600
Ru
nn
ing
tim
e in
sec
on
ds
Type of event
System log
ARC2
JENA
0
1
2
3
4
Ru
nn
ing
tim
e in
sec
on
ds
Type of event
Web Server logARC2
JENA
0
0.5
1
1.5
2
2.5
3
1 0 1 0 0 2 0 0 4 0 0
TH
E R
UN
NIN
G T
IME
IN S
EC
ON
DS
NO. OF ITEM
Application, 6,627 events
Security, 13,097 events
System, 42,961 events
Web Server, 376 events
FMEA; 414 processes
Six Sigma-based KM and Its Application in IT. Systems Management
17 | P a g e
of LogSig, SLCT, and SELO on the different numbers of log messages, from the different
numbers of log messages.
Figure 6-32. The parsing execution time of SELO, SLCT, and LogSig
The charts show an impressive performance of SLCT method in parsing event logs. It
consumes a very short interval of time to parse 40,000 log messages in three log sets.
Compared to SLCT, SELO Parser reveals a similar performance since our proposed Parser
consumes 2 seconds in maximum to parse 40,000 log messages in three event logs. The
parsing execution speed of SELO Parser may be a bit slower than SLCT method, but much
faster than LogSig method. Moreover, in a range of 600 to 4,800 log messages, SELO Parser
reveals a better performance than SLCT (Figure 6-32).
b. Accuracy
Accuracy is evaluated based on a comparison between the number of events found in
experimental results and the number of events counted in Event Viewer. The experimental
results represent an absolute accuracy (100%) of SELO model in parsing and querying log
messages to / from knowledge base of SELO. The experiemt results also show that the
proposed Parser archive a high accuracy compared to the similar approaches.
6.4 SELO Knowledge Base
6.4.1 Validate SELO based OntoQA technique
In order evaluate and validate an ontology, we used OntoQA technique proposed by
the authors in (Tartir, Arpinar, & Sheth, 2010). The techniqua used a set of characteristics
measuring different aspects of an ontology and the knowledge base built by the ontology.
Evaluation of SELO schema
SELO schema is evaluated based on its the richness, width, depth, and inheritance.
- Relationship Richness
0.01
0.1
1
10
100
6 0 0 1 2 0 0 2 4 0 0 4 8 0 0 9 6 0 0
RU
NN
ING
TIM
E IN
SEC
ON
DS
NO. OF EVENT
SELO (Application)SELO (Security)SELO (System)SLCTLogSig
Lucian Blaga University of Sibiu
18 | P a g e
𝑹𝑹𝑺𝑬𝑳𝑶 =|𝑷|
|𝑯| + |𝑷|=
|𝟔|
|𝟑| + |𝟔|= 𝟎. 𝟔𝟕
- Attribute Richness
𝑨𝑹𝑺𝑬𝑳𝑶 =|𝒂𝒕𝒕|
|𝑪|=
𝟑𝟔
𝟗= 𝟒
Evaluation of SELO knowledge involved.
- Class Richness
𝑪𝑹𝑺𝑬𝑳𝑶 =|𝑪′|
|𝑪|=
|𝟖|
|𝟗|= 𝟎. 𝟖𝟗
- Class Connectivity
𝑪𝑪𝒐𝒏𝑺𝑬𝑳𝑶(𝑪𝒊) = |𝑵𝑰𝑹𝑬𝑳(𝑪𝒊)|
- Class Importance
𝑪𝑰𝑺𝑬𝑳𝑶 =|𝑰𝒏𝒔𝒕(𝑪𝒊)|
𝑲𝑩(𝑪𝑰)|
Figure 6-35. The importance of classes in SELO
- Relationship Richness
For SELO, this measure is 8 / 9 * 100% = 89%.
6.4.2 Based on the Similar Approaches
SELO reveals some outstanding aspects that are not found in other approaches. First,
SELO supports knowledge management of server events. Its approach is to rely on FMEA
methodology that allows creating FMEA reports to support to the system administrators in
determining feasible solutions for error events. Based on Ontology, SELO illustrates
excellently knowledge of server events for computer users. This may help them to not only
learn quickly the knowledge of event logs but also construct their own knowledge bases for
Six Sigma-based KM and Its Application in IT. Systems Management
19 | P a g e
the purpose of share and reuse. Second, SELO provides several tools supporting Ontology
development and knowledge inference. Third, an approach to populate automatically
instances of SELO without human intervention is proposed. Although a similar approaches is
found in (Rehman & Claudiu, 2016), their approach aims at only preserving knowledge
created during FMEA deployment. SELO facilitates operations to its users and provides a
procedure of ontology development based on the popular methods of ontology development
such as METHODOLOGY and 101 method.
6.5 An Evaluation of OKMD model Based on Experts’ Opinion
6.5.1 The Survey’s Parameters
The survey questionnaire is sent to 49 participants who have knowledge in the fields
of Six Sigma, quality improvement or engineering, and IT in 5 weeks. The age of participants
is between 24 years of age and 47 years of age. They are the experts (49% of respondents),
professors (12%), Ph.Ds. (18%), and Ph.D. candidates and students (20%). 63% of
respondents belongs to the ones who are working in the fields of Six Sigma or engineering
(quality improvement) while the rests work in the IT. In Romania, 37 respondents are
collected from 24 experts, 04 professors, 01 Ph.D., and 08 Ph.D. candidates and student. In
Vietnam, 12 respondents come from 02 professor, 03 doctors, and 07 Ph.D. candidates.
6.5.2 The Results and Discussion
a. Evaluation based on sustainable criteria of KM, quality attributes and successful
aspects of Six Sigma projects
Figure 6-36. Evaluation of the quality attributes for KPD and OKMD
In order to rate the quality parameters of KPD, a table of ranking and rating
(Subramanian & Geetha, 2012) is applied (Table 6-17). Based on the table, the higher the
total score of KPD quality parameters, the better the usability of KPD. The Figure 6-36
4.29
4.16
4.1
4.1
3.96
3.9
3.5 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 4.4
Usability
Usefulness
User satisfaction and expert ranking
Relevance
Security
Economical
Lucian Blaga University of Sibiu
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represents the overall summary weightage for the quality attributes/parameters. On the basis
of the ranking and rating table (Table 6-17) and the parameters, it is clear that KPD is an
extremely usable model that can be applied effectively in DMAIC deployment, with the
overall evaluation score of 4.2 (Table 6-16) though the aspects of economy and Security
should be improved from the lowest scores (3.9 and 3.96 respectively).
b. Evaluation based on expert’s opinion
On the basis of survey results, expert’s opinion is analysed to validate the proposed
model on the basis of sucessful aspects of Six Sigma, sustainable criterial of knowledge
management, and usefulness of the proposed knowledge portal.
Figure 6-37 Areas and experience of survey participants.
On the bases of the working or researching fields and the number of experience year,
we asked the respondents’ opinion on effectiveness of the KPD model, which is a
concretization of OKMD model, in different aspects.
Figure 6-39. Cost, Time, and Use Ease Effectiveness based on survey results
14%
45%
20%
18%
2%
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Information Technology
Six Sigma
Knowledge Management
Quality improvement
Other
1.531.33
0.51
3.473.67
2.76
0.00
1.00
2.00
3.00
4.00
Cost effectiveness Time effectiveness Ease of use
Rat
ing
fro
m 1
to
5
Effeciveness of OKMD
Pure DMAIC process KM-based DMAIC process
Six Sigma-based KM and Its Application in IT. Systems Management
21 | P a g e
The survey result is revealed in Figure 6-39. The first outstanding feature found easily
is that the proposed model will enhace the use, time and costs effectiveness of DMAIC
processes. Most of respondents agree that the model can mitigate the deployment costs and
time of DMAIC processes, and it is easy to apply into Six Sigma projects, with the ratings
3.47, 3.67, and 2.76 respectively.
The approbation of the participants are also illustrated in the different aspects in the
survey. Over 86% of respondents seconded that Six Sigma-based knowledge management
will support employees and experts in quickly accessing the available knowledge resource.
Althogh the rest of respondents were not sure if the model can support that or not (under
14%), they did not refuse it (Figure 6-40). Therefore, over 84% of respondents found agreed
that the improvement skill of new employees will be enhanced relying on knowledge created
by the past DMAIC processes (Figure 6-41). Also, lots of participants wapproved that SSKM
will improve quality of Six Sigma project on the basis of available knowledge (Figure 6-43)
and contribute knowledge to innovation and solutions of improvement Figure 6-44with just
over 80% and 88% respectively though onlye 4% of respondents completely disagree this
point of view.
Finally, an evaluation on the level of understanding the proposed model was
conducted. Those who gained a good understand of KPD taken account the highest percentage
of respondents (37%). With a very good understanding of KPD, 10% of resondents was found
in the survey results. The rest of responses are divided into the remaining groups of
respondents with 31% (Basic understanding) and 22% (Average understanding) respectively.
Chapter 7. CONCLUSION, CONTRUBITION, AND FUTURE WORKS
7.1 Research findings
This research uncover several findings related to the proposed model and tools
including Six Sigma theory, Six Sigma tools, Six Sigma-based reports, processes of
knowledge management, and integrated models of Six Sigma’s tools and knowledge
management proposed in recent years. Another finding to note is that tools such Parser and
Reasoner are indispensable ones to support knowledge management in order to construct a
knowledge base as well as retrieve knowledge from the knowledge base.
Ontology-based knowledge representation is an effective method to apply into Six
Sigma-based knowledge management.
IT system is considered as a heart of organizational activities. The breakdowns or
outages of the system result in loss of repaired costs and efforts, and great damage in an
organization. In order to enhance availability of an IT system, the important components of
the system including servers should be always stable and ready to respond all requests from
Lucian Blaga University of Sibiu
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IT system. A knowledge base of server failures provides them with valuable knowledge,
access and sharing, and help them to improve their capacity of preventing, detecting,
resolving, and eliminating server problems on IT systems.
7.2 Research Contributions
This research is conducted at “Lucian Blaga” university of Sibiu, Sibiu, some
companies in Sibiu, Romania, and Quy Nhon university, Viet Nam. The theoritical research
was conducted in “Lucian Blaga” university of Sibiu while data collection was fulfilled at
Quy Nhon university in Viet Nam. The results presented in this thesis are the outcome of the
three years of research and include contributions on theory, practice, and science.
7.2.1 Theoretical Contribution
- The procedures of KM and their activities have been investigated in order to
determine suitable elements for applying into the field of Six Sigma and IT sector.
- A model of Six Sigma-based knowledge management has been proposed to
accumulate and share knowledge created by DMAIC processes.
- A model of Six Sigma-based server knowledge management has been proposed to
enhance IT systems management.
- An analysis of Parsers and Reasoners has been conducted, and therefore support to
developing knowledge bases from server event logs.
- The algorithms for a Parser used to automatically generate instances of an ontology
from an event log of Windows server.
7.2.2 Practical Contributions
- A Knowledge Portal (i.e. KPD) to support models of Six Sigma-based knowledge
management.
- An ontology of server events built in Protégé with classes, properties and constraints
of properties in order to achieve a consistency of knowledge.
- A Log Parser (i.e. SELO Parser) written by PHP language and integrated with KPD
to support to automatically convert all Windows OS-installed server event logs
(EVT or EVTX formats) to the knowledge base SELO.
- A inference module (i.e SELO Reasoner) written in PHP language and used to
retrieve knowledge from the knowledge base SELO.
- A questionnaire-based survey has been conducted. Therby, they survey help the
participants to improve their knowledge of a model of Six Sigma-based knowledge
management and tools that can be applied into Six Sigma projects.
7.2.3 Scientific Contributions
From the research results of this thesis, we have contributed some international
publications including 3 (three) international journal articles (one published, two under
Six Sigma-based KM and Its Application in IT. Systems Management
23 | P a g e
review) and 6 (five) international conference papers (one under review). The contents of most
of papers and journal articles are included in this thesis.
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